Historical Stock Research Articles

Machine Learning Project in Python to Predict Stock Price

The Stock Price Prediction System is an innovative application of machine learning and data analysis techniques aimed at forecasting the future prices of publicly traded stocks. Stock markets are dynamic and influenced by a multitude of factors, making stock price prediction a complex and challenging task. This system leverages historical stock price data, along with relevant financial and macroeconomic indicators, to provide accurate and valuable predictions for investors, traders, and financial analysts. Index Terms Data Collection and Preprocessing, Machine Learning Models based, Prediction and Visualization, Real-time Update.

Tesla Stock Prediction Based on Twitter Comments’ Sentiment Feature Extraction and Ensemble Networks

As people become more aware of environmental issues, Tesla, a pillar of the global electric vehicle market, has become a hot commodity in recent years. The prediction of Tesla’s share price is also a hot topic in the investment market. This experiment extracts sentiment factors of tweets about Tesla’s comments, combined with Tesla’s historical stock price as the dataset for training, testing and inspection. This experiment builds time series prediction models based on LSTM, XGBoost and RF algorithms to predict Tesla’s stock price, and evaluates the prediction effectiveness of the three algorithms based on the fit and error of the prediction results. The analysis of the data shows that XGBoost has the best fit and the lowest error among the three algorithms, and that the sentiment factor has its unique utility as raw data. The experimental results also empirically demonstrate the applicability of sentiment factor analysis and the three algorithms LSTM, XGBoost and RF in the field of stock price prediction.

Stochastic Calculus and Its Impact On Analyzing Option Pricing

This research study the behaviour of the correction of Black-Scholes portfolios based on historical stock price data. We build a model that simulates one sample path of the stock price stochastic process at discrete time steps and track the correction over a time interval as it relates to the change in stock price over time. We also study the effect that relaxing this assumption has on the self-financing property of the replicating portfolio. We show that as the frequency of the time steps increases, the correction is more likely to be close to 0. We also show that the majority of historical stock return series that studied have caused the replicated portfolio to have a positive correction. We conclude that the Black-Scholes model can be used to find the no-arbitrage rational price for an option, a financial instrument that derives its value from the value of an underlying asset.

Studying Stock Prediction in the Context of Machine Learning Exemplified by Analyzing SVM and LSTM Models

The impact of stocks on economic development is significant, with most companies employing quantitative finance methods to analyze and trade stocks. In the present day, there is a growing plethora of stock prediction methods, requiring individuals to carefully select the appropriate means of prediction. This study aims to present the highly regarded machine learning algorithms of recent years. Machine learning enables the extraction of trading strategies by learning patterns from historical stock data. Deep learning, a subset of machine learning, eliminates the need for researchers to manually extract features, instead utilizing neural network models that have proven effective for stock analysis. This paper also provides an extensive analysis of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), elucidating their algorithmic principles and assessing their strengths and weaknesses based on the findings of other researchers. It is observed that SVM is effective for predicting small quantities, but lacks suitability for non-stationary data prediction due to the non-informative nature of financial time series data, which significantly diminishes the accuracy of SVM predictions. In contrast, LSTM emerges as the most comprehensive single algorithm, exhibiting high accuracy and efficiency across all aspects. The paper also explores advanced hybrid models, which demonstrate that combining the advantages of multiple models can lead to higher accuracy and efficiency. The future of stock prediction algorithms holds promise for development, with the potential to construct an intelligent system that automatically selects data for analysis.

Optimizing Stock Price Prediction: Exploring LSTM Architectural Parameters in Financial Forecasting

Accurate stock price prediction is a crucial concern in the field of finance, impacting investors, traders, and the broader economic landscape. Traditional time series analysis and statistical models often fall short in capturing the dynamic and nonlinear nature of financial markets. Financial market forecasting has advanced significantly with the advent of deep learning technology, especially Long Short-Term Memory (LSTM) networks. This study conducts an in-depth exploration of LSTM networks' critical architectural parameters and their impact on stock price prediction. The performance of the LSTM-based model is assessed using historical stock price data from Amazon, with particular attention paid to hyperparameters like the number of neurons, dropout rates, and LSTM layers. The research reveals that a well-balanced model architecture with moderate units per layer (e.g., 64), a single layer, and a dropout rate of 0.7 is key to achieving accurate predictions in financial markets. The outcomes of this research hold practical significance for both investors and financial analysts. Furthermore, they emphasize the promising potential of LSTM networks in the realm of financial forecasting, opening avenues for future studies in finance. This investigation deepens the comprehension of LSTM network architecture and its applicability in predicting stock prices, providing valuable insights that extend beyond the finance sector.

ANALISIS ABNORMAL RETURN DAN TRADING VOLUME ACTIVITY UNTUK MENGUKUR TERJADINYA JANUARI EFFECT PADA KELOMPOK SAHAM INDEKS LQ 45 DI BEI

ANALISIS ABNORMAL RETURN DAN TRADING VOLUME ACTIVITY UNTUK MENGUKUR TERJADINYA JANUARI EFFECT PADA KELOMPOK SAHAM INDEKS LQ 45 DI BEI

Survey on Stock Price Forecasting

This research project delves into the challenging domain of stock price prediction by harnessing the capabilities of neural networks. In an ever-evolving financial landscape, the project seeks to develop a robust model that can analyze historical stock data, identifying intricate patterns and trends to inform future price movements. Through the convergence of data science and financial analysis, this endeavor aims to provide investors with valuable insights, aiding them in navigating the complexities of the stock market. Employing machine learning algorithms, particularly neural networks, the model scrutinizes extensive sets of historical stock data, considering factors such as past price trends, trading volumes, and relevant financial indicators. Techniques such as regression, time-series analysis, and neural networks are leveraged to unveil complex relationships within the data, enhancing the model's predictive capabilities. The model undergoes meticulous training on historical data to glean insights from past market behaviors. Subsequently, it undergoes rigorous testing on unseen data to evaluate its predictive accuracy. Continuous refinement and optimization strategies are implemented to ensure the model's adaptability to the ever-changing dynamics of the market. Ultimately, this project aspires to furnish investors with a powerful tool for making informed decisions in the face of the unpredictable nature of financial markets. By integrating the precision of neural networks with the intricacies of stock market analysis, the research aims to contribute to the advancement of stock price forecasting methodologies.

Comparative Analysis Of Stock Performance Before And After Determining Asymmetric Auto Rejection In Companies Listed On The Idx

ABSTRACTThe asymmetric auto rejection policy has an impact on stock performance, namely influencing investors' perceptions of the security and quality of order execution. Comparing stock performance before and after establishing asymmetric auto rejection can provide benefits for market players, namely evaluating effectiveness in improving market integrity and protecting investors. The aim of this research is to analyze the comparison of stock performance before and after the establishment of asymmetric auto rejection in companies listed on the IDX. The method used in this research is quantitative by collecting data in the form of historical stock data before and after the asymmetric auto rejection policy. The data analysis in this research used statistical tests with the help of SPSS version 26, namely paired t-test samples. The results of this research show that there is no significant difference between stock performance before the asymmetric auto rejection policy was implemented and stock performance after the asymmetric auto rejection policy was implemented. This is because the average value is only different by 0.033. Apart from that, the significance value is 0.975. This means that if it is greater than 0.05, H0 is accepted and H1 is rejected.

Stock Price Prediction Using Machine Learning Models: A Study of NSE Stocks

Abstract: This paper presents a study on stock price prediction utilizing the ARIMA and Linear Regression algorithms, focusing on companies listed on the National Stock Exchange (NSE) of India. The aim is to compare the predictive accuracies of these models while considering the lifetime data of stocks obtained through the Yahoo Finance API. Through comprehensive analysis, historical stock data spanning the entire lifespan of the stocks is utilized, enabling a thorough exploration of long-term trends and patterns. It was inferred that for NSE (Indian Company) stocks and Linear Regression prove to be more efficient than ARIMA. The research methodology involves data retrieval, preprocessing, and model training, with Python being the primary programming language for implementation. Findings indicate the effectiveness of ARIMA and Linear Regression models in forecasting NSE stock prices, with implications for financial decision-making and investment strategies. This study contributes to the understanding of machine learning applications in the stock market domain, emphasizing the importance of leveraging comprehensive historical data for enhanced predictive performance.

The Influence of Social Media on Stock Market: A Transformer-Based Stock Price Forecasting with External Factors

This paper introduces an innovative approach to forecasting stock prices. Forecasting stock prices is crucial in assisting investors in making informed decisions. Our research presents a unique method that utilizes transformer-based machine learning approach for stock price forecasting. This method exploits self-attention mechanisms to grasp intricate patterns and dynamics within historical stock price data. To bolster our model performance, we integrate investors’ sentiment collected from social media by using sentiment analysis with the help of natural language processing. Utilizing the variation caused by investors’ sentiment over time, as well as external macroeconomic factors, our proposed model outperforms benchmark models. Through extensive comparisons with various benchmark machine-learning algorithms, results produced by our proposed method are favorably comparable to those produced by conventional approaches. Across multiple machine learning models, our preferred model demonstrates superior performance, achieving an RMSE value of 0.96 compared to the RMSE value of 1.58 obtained from LSTM model.

An interval constraint-based trading strategy with social sentiment for the stock market

Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news media data, expert comments, social opinion data, and pandemic text data using natural language processing, and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices. An interval constraint-based trading strategy is constructed. Using data from several typical stocks in the Chinese stock market during the COVID-19 period, the empirical studies and trading simulations show, first, that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting. Second, the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus, can assist investors in decision-making.

Estimating Stock Market Betas via Machine Learning

Abstract Machine learning-based stock market beta estimators outperform established benchmark models both statistically and economically. Analyzing the predictability of time-varying market betas of U.S. stocks, we document that machine learning-based estimators produce the lowest forecast and hedging errors. They also help to create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random forests perform the best overall. Model complexity is highly time-varying. Historical stock market betas, turnover, and size are the most important predictors. Compared to linear regressions, allowing for nonlinearity and interactions significantly improves predictive performance.

PREDIKSI HARGA SAHAM MENGGUNAKAN ALGORITMA NEURAL NETWORK

In recent years, the development of technology and artificial intelligence has brought forth new opportunities in analyzing and predicting stock prices. One of the approaches used is the Neural Network algorithm, which is a part of the branch of artificial intelligence known as Deep Learning. This algorithm can learn complex patterns and relationships among data by modeling inspired by the human neural network. This research utilizes the Neural Network for stock price prediction and aims to understand the application of Neural Network in predicting stock prices, which can benefit investors and market participants. Additionally, historical stock price data can be used as input for the Neural Network algorithm. The Neural Network is a frequently used algorithm for accurate predictions and is widely employed in prediction-based or forecasting research. The result of this research is the Root Mean Squared Error (RMSE) value of 19.734 +/- 0.000. The use of the Neural Network as an algorithm for stock price prediction provides investors with valuable information for making investment decisions for companies..

Performance Analysis of Share Market by Using ML Technique

This research paper delves into the intricate world of stock market analysis, exploring the application of machine learning techniques to enhance performance evaluation. The study adopts multifaceted approach, combining historical stock market data, advanced machine learning algorithms, and rigorous back testing to derive insights into market dynamics. The findings aim to contribute to the growing body of literature on financial analytics, providing valuable implications for investors and financial professionals.

Performance Analysis of Share Market by using ML Technique

This research investigates the transformative impact of machine learning (ML) on stock market analysis, assessing its potential benefits and confronting associated challenges. The study employs various ML models, including regression, classification, clustering, and natural language processing, to analyze extensive datasets comprising historical stock prices, financial indicators, economic data, and sentiment from social media. Key findings reveal that ML models exhibit superior accuracy in forecasting market movements and stock prices compared to traditional methods. Automated anomaly detection algorithms demonstrate proficiency in identifying unusual market behaviour, offering timely warnings for potential market shifts and fraudulent activities. ML-powered risk assessment tools showcase the capacity to personalize investment strategies based on individual preferences, augmenting decision-making for investors. Despite challenges such as data quality, model selection, and ethical considerations, the research underscores the undeniable potential of ML in stock market analysis. Rigorous methodologies, including data preprocessing, feature engineering, and model evaluation, contribute to the robustness of the findings. Ethical considerations, including algorithmic biases and transparency are thoroughly explored to ensure responsible application in the financial domain.

Time-Series Analysis and Forecasting of S&P500 Index Based on ARIMA and ETS Model

The study embarks on an insightful journey into the world of stock indices through the deeper understanding of time series analysis application, especially during the special period of oil price volatility. The goal of this paper is to utilize sophisticated statistical models, including Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ETS), to extract meaningful information from historical stock index data of S&P500 by Yahoo Finance, enhancing predictive accuracy and operating forecasts to inform strategic decision-making. In addition to the prediction results, the study also compares the forecasts of the two models through some values, and concludes that the prediction effect of the ARIMA model is better than that of the ETS model. The reason behind this result has a lot to do with the processing of the data itself and the fit of the models. For S&P500 during the period of the study, the ARIMA model’s prediction result is better. The insights derived from this analysis are expected to empower investors, researchers, and market analysts with a deeper understanding of the stock index’s past behavior and its implications for future performance.

Research on Risk Measurement in Chinese Stock Market - Based on GARCH-VaR Modeling

Chinese stock investors, whether he is an individual investor or working for a investment company, are always worried about the risk of their asset, it is difficult to measure the risk without a functional model, thus, using a model to solve it is of the pivot. The initial aim of this paper is to measure and predict the risk of Chinese stock market using GARCH-VAR model. By analyzing historical stock index return data, this paper develops a dynamic model to capture the volatility and risk correlation of the stock market. The comprehensiveness and accuracy of the results are ensured by using recent data, including stock indices that broadly cover different industries and market capitalization. The findings suggest that significant volatility and risk correlations exist in the Chinese stock market. This paper provides an effective methodology to measure and predict the level of risk in the stock market, which provides investors and risk management organizations with valuable references and decision-making bases and is important for understanding and managing the risk in the Chinese stock market, which helps stock investors to make informed optimal decisions on risk management and asset allocation.

Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.

Enhancing Stock Price Prediction with Deep Cross-Modal Information Fusion Network

Stock price prediction is considered a classic and challenging task, with the potential to aid traders in making more profitable trading decisions. Significant improvements in stock price prediction methods based on deep learning have been observed in recent years. However, most existing methods are reliant solely on historical stock price data for predictions, resulting in the inability to capture market dynamics beyond price indicators, thus limiting their performance to some extent. Therefore, combining social media text with historical stock price information has proposed a novel stock price prediction method, known as the Deep Cross-Modal Information Fusion Network (DCIFNet). The process is initiated by DCIFNet, which employs temporal convolution processes to encode stock prices and Twitter content. This ensures that each element has sufficient information about its surrounding components. Following this, the outcomes are inputted into a cross-modal fusion structure based on transformers to enhance the integration of crucial information from stock prices and Twitter content. Lastly, a multi-graph convolution attention network is introduced to depict the relationships between different stocks from diverse perspectives. This facilitates the more effective capturing of industry affiliations, Wikipedia references, and associated relationships among linked stocks, ultimately leading to an enhancement in stock price prediction accuracy. Trend prediction and simulated trading experiments are conducted on high-frequency trading datasets spanning nine different industries. Comparative assessments with the Multi-Attention Network for Stock Prediction (MANGSF) method, as well as ablation experiments, confirm the effectiveness of the DCIFNet approach, resulting in an accuracy rate of 0.6309, a marked improvement compared to representative methods in the field.

Pengaruh Moving Average dan Transaction Volume pada Return Saham Perbankan Indonesia

This study investigates the impact of Moving Average (MA) Stock prices and Transaction Volume on Stock returns in the Indonesian banking sector, specifically focusing on the LQ45 index from 2020 to 2023. It delves into the concepts of Efficient Market Hypothesis (EMH), Behavioral Finance, and Technical Analysis. EMH posits that Stock prices reflect all available information and are hard to consistently beat, while Behavioral Finance suggests that psychological factors can lead to market inefficiencies exploitable by technical analysis. Employing a quantitative approach and data from the Indonesia Stock Exchange (BEI), the study finds that MA and Transaction Volume positively and significantly influence Stock returns in the banking sector. However, variations exist in individual bank samples due to factors like fundamentals and unique behaviors, which cannot be solely determined from historical Stock prices. Thus, the research underscores the importance of considering additional factors beyond past price trends when analyzing the Stock performance of individual banks.